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Sep 19, 2014
09/14

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Bruno Olshausen

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This is a recorded lecture given by Prof.Bruno Olshausen as part of the VS265 class taught at UC Berkeley.

Topics: VS265, Neural Computation, UC Berkeley, Redwood Center

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Lecture 4 (Sept 8) of UC Berkeley Vision Science 265: Neural Computation taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Signal detection and amplification Computing with chemistry/allostery Phototransduction

Topics: theoretical neuroscience, phototransduction, signal detection

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Nov 1, 2014
11/14

by
Bruno Olshausen

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This is a recorded lecture given by Prof.Bruno Olshausen as part of the VS265 class taught at UC Berkeley.

Topics: VS265, Neural Computation, UC Berkeley, Redwood Center

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Oct 4, 2014
10/14

by
Fritz Sommer

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UC Berkeley, VS265, Bruno Olshausen, Fritz Sommer, Associative Memories

Topics: UC Berkeley, VS265, Bruno Olshausen, Fritz Sommer, Associative Memories

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Lecture 11 (Oct 1) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Neural coding in Lateral Geniculate Nucleus (LGN) (Lecture given by Fritz Sommer)

Topics: theoretical neuroscience, Lateral Geniculate Nucleus, neural coding

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Lecture 8 (Sept 22) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Retinal tiling Cone tiling Subdivision of labor by midget and parasol RGC’s Foveated sampling

Topics: theoretical neuroscience, retina

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Lecture 1 (Aug 27) of UC Berkeley Vision Science 265: Neural Computation taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topics is Introduction Theory and modeling in neuroscience Goals of AI/machine learning vs. theoretical neuroscience

Topics: theoretical neuroscience, machine learning

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Lecture 23 (Nov 12) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Project mini-presentations

Topic: theoretical neuroscience

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Lecture 25 (Nov 19) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Supervised learning (and cerebellum) Delta learning rule and back-propagation Supervised learning in cerebellum Kanerva’s Sparse Distributed Memory Model

Topics: theoretical neuroscience, supervised learning, cerebellum, sparse distributed memory, delta...

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Lecture 5 (Sept 10) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Physics of computation Horizontal cells and lateral inhibition; Optimal pooling by bipolar cells Analog VLSI and silicon retina

Topics: theoretical neuroscience, retina, lateral inhibition

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Lecture 2 (Sept 1) of UC Berkeley Vision Science 265: Neural Computation taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ This lecture (in two video files) is about Brains Mammalian brain organization Insect and spider brains

Topics: neuroscience, brain organization

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Lecture 15 (Oct 15) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topics are Organization, topography Horizontal connections Self-organizing maps Manifold models

Topics: theoretical neuroscience, self-organizing maps

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Lecture 26 (Nov 24) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Reinforcement learning and basal ganglia (lecture by Sophia Sanborn)

Topics: theoretical neuroscience, reinforcement learning, basal ganglia

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Lecture 20 (Nov 3) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Inference – denoising, dynamical models Denoising with a sparse coding prior Dynamical models

Topics: theoretical neuroscience, inference, dynamic models

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Lecture 18 (Oct 27) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Attractor dynamics (continues lecture 17 given Oct 22) Hopfield networks, memories as ‘basis of attraction’ Line attractors and `bump circuits’

Topics: theoretical neuroscience, Hopfield networks, attractor dynamics

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Lecture 10 (Sept 29) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Starburst amacrine cells and the computation of motion (lecture by Rowland Taylor)

Topics: retina, motion detection, amacrine cells, theoretical neuroscience

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Lecture 27 (Dec 1) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Hyperdimensional computing Local vs. distributed representation Binding, bundling and sequencing Computing in superposition

Topics: theoretical neuroscience, hyperdimensional computing

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Lecture 29 (Dec 8) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is N eural oscillations and synchrony Binding by synchrony Coupled oscillator networks Computing with waves This is the final lecture of the class.

Topics: theoretical neuroscience, neural oscillations, synchrony

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Lecture 17 (Oct 22) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Attractor dynamics Hopfield networks, memories as ‘basis of attraction’ Line attractors and `bump circuits’

Topics: theoretical neuroscience, hopfield networks, attractor dynamics

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Lecture 6 (Sept 15) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Neural coding: Spiking neuron models Signal compression in retina: theory of retinal whitening

Topics: theoretical neuroscience, retina, neural coding

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Lecture 16 (Oct 20) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topics are Organization, topography (continues lecture 15 given Oct 15) Horizontal connections Self-organizing maps Manifold models

Topics: theoretical neuroscience, self-organizing maps

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Lecture 22 (Nov 10) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Hierarchical models and invariance (Lecture is by Sophia Sanborn; topic continues lecture 21 given Nov 5) Learning of higher-order structure, part-whole relationships Models of invariance Three-way interactions/dynamic routing Complex cells, power spectrum vs. bispectrum

Topics: theoretical neuroscience, hierarchical models

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Lecture 21 (Nov 5) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Hierarchical models and invariance Learning of higher-order structure, part-whole relationships Models of invariance Three-way interactions/dynamic routing Complex cells, power spectrum vs. bispectrum

Topics: theoretical neuroscience, hierarchical models

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Lecture 24 (Nov 17) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Perception-action loop Active perception Representations for perception vs. action Thalamocortical loop as sensorimotor loop

Topics: theoretical neuroscience, perception-action loop

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Lecture 3 (Sept 3) of UC Berkeley Vision Science 265: Neural Computation taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Neural mechanisms and models Membrane equation, compartmental model of a neuron Shunting inhibition, NMDA, dendritic nonlinearities Perceptron model

Topics: theoretical neuroscience, neuron models, perceptron model, neuroscience

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Lecture 19 (Oct 29) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Probabilistic models Learning and inference in generative models Boltzmann machines Restricted Boltzmann machines and Energy-based models

Topics: theoretical neuroscience, generative models, boltzmann machines

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Lecture 3 of Vision Science 265, Fall 2010 Semester at UC Berkeley.

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Lecture 7 (Sept 17) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Neural coding (continues lecture 6 given Sept 15) Spiking neuron models Signal compression in retina: theory of retinal whitening

Topics: theoretical neuroscience, neural coding

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Lecture 11 of Vision Science 265, Fall 2010 Semester at UC Berkeley. Review of past and overview of future topics, answer questions, learning sparse codes.

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Lecture 9 (Sept 24) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Auditory coding Cochlea and auditory nerve Time-frequency analysis Phase and amplitude coding by spikes ICA of natural sound

Topics: theoretical neuroscience, auditory coding

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Lecture 12 (Oct 6) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Inference LGN and Cortex Overcomplete representation in primary visual cortex Hebbian learning and PCA Sparse coding model of V1

Topics: theoretical neuroscience, sparse coding, hebbian learning

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Lecture 7 of Vision Science 265, Fall 2010 Semester at UC Berkeley. Paul Ivanov gives tips about programming assignments using Python.

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Lecture 13 (Oct 8) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Inference (continuation of lecture 12 given Oct 6) LGN and Cortex Overcomplete representation in primary visual cortex Hebbian learning and PCA Sparse coding model of V1

Topics: theoretical neuroscience, sparse coding, hebbian learning

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Lecture 28 (Dec 3) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is hyperdimensional computing (lecture by Spencer Kent and Navneedh Maudgalya; continues lecture 27 given Dec 1) Local vs. distributed representation Binding, bundling and sequencing Computing in superposition

Topics: theoretical neuroscience, hyperdimensional computing

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Lecture 15 of of Vision Science 265, Fall 2010 Semester at UC Berkeley. more about Hopfield networks (capacity, analog circuit version, demonstration of convergence); extensions to Hopfield networks to other areas include stereo disparity matching and position invariant pattern recognition.

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Lecture 14 (Oct 13) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Inference (continues lectures 12 & 13 given Oct 6 & Oct 8) LGN and Cortex Overcomplete representation in primary visual cortex Hebbian learning and PCA Sparse coding model of V1

Topics: theoretical neuroscience, sparse coding, hebbian learning

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Lecture 24 of Vision Science 265, Fall 2010 Semester at UC Berkeley. More about ICA (Independent Components Analysis) and Kalman filters. Coding in Spiking neurons.

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Lecture 5 of Vision Science 265, Fall 2010 Semester at UC Berkeley. Speaker is Tony Bell. Title of talk is: Emergence and submergence in the nervous system.

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Lecture 23 of Vision Science 265, Fall 2010 Semester at UC Berkeley. More about Independent Components Analysis, topographic ICA, and ICA's relationship to sparse coding, ICA assumptions. Kalman Filter.

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Nov 20, 2014
11/14

by
Bruno Olshausen

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This is a recording of the lecture from Prof.Bruno Olshasuen's class for the VS265 class, held at UC Berkeley. This lecture had some technical issues and the sound is missing for the first few minutes of the lecture.

Topics: VS265, Fall14, Redwood Center, Bruno Olshausen

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Lecture 22 of Vision Science 265, Fall 2010 Semester at UC Berkeley. Review of Boltzmann Machines lab assignment and a bit on Restricted Boltzmann Machines (RBMs). Also, Independent Components Analysis and ICA's relationship to probabilistic sparse coding.

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Lecture 10 of Vision Science 265, Fall 2010 Semester at UC Berkeley. Speaker is Bruno Olshausen. Shows running matlab programs for problems due this week demonstrating hebbian learning and PCA. Different coding schemes, including sparse coding, relationship between competitive learning and k-means clustering, coding in retinal ganglion cells, LGN and area V1. Gabor functions.

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Lecture 12 of of Vision Science 265, Fall 2010 Semester at UC Berkeley. Demonstrate solution of assignment problems (learning sparse codes), self organizing feature maps.

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Lecture 21 of Vision Science 265, Fall 2010 Semester at UC Berkeley. In the first half of class, students describe their proposed class projects. Second half of class: more on derivation of Boltzman machine learning rule.

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Lecture 4 of Vision Science 265, Fall 2010 Semester at UC Berkeley. Topics covered are perceptrons, derivation of the delta learning rule for single layer neural networks and the back-propagation learning rule for multi-layer networks.

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Lecture 13 of of Vision Science 265, Fall 2010 Semester at UC Berkeley. Kohonen self organizing maps, Michael Stryker model of ocular dominance columns formation, manifolds, experiments showing adapting to a face lowers the threshold for detecting the opposite type face.

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Lecture 2 of Vision Science 265, Fall 2010 Semester at UC Berkeley.

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Lecture 9 of Vision Science 265, Fall 2010 Semester at UC Berkeley. Speaker is Bruno Olshausen. Described PCA, eigenvalue decomposition of a matrix, the relationship between PCA and hebbs rule, whitening.

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First lecture of Vision Science 265, Fall 2010 Semester at UC Berkeley.

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Lecture 26 of Vision Science 265, Fall 2010 Semester at UC Berkeley. Guest speaker is Jeff Hawkins, founder of Numenta. Describes their latest work on hierarchical temporal memories. Paper about it is at: http://www.numenta.com/htm-overview/education/HTM_CorticalLearningAlgorithms.pdf Jeff is the author of the book "On Intelligence," co-authored with Sandra Blakeslee: http://www.onintelligence.org/